Systems, methods, and devices for generating a corrected image

ABSTRACT

Systems, methods, and devices for generating a corrected image are provided. A first robotic arm may be configured to orient a source at a first pose and a second robotic arm may be configured to orient a detector at a plurality of second poses. An image dataset may be received from the detector at each of the plurality of second poses to yield a plurality of image datasets. The plurality of datasets may comprise an initial image having a scatter effect. The plurality of image datasets may be saved. A scatter correction may be determined and configured to correct the scatter effect. The correction may be applied to the initial image to correct the scatter effect.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.17/464,410, filed on Sep. 1, 2021, which application is incorporatedhere by reference in its entirety.

FIELD

The present technology generally relates to imaging and imageprocessing, and relates more particularly to generating a correctedimage using a scatter correction.

BACKGROUND

Imaging devices may be used by a medical provider for diagnostic and/ortherapeutic purposes. Images obtained from such imaging devices mayinclude defects such as noise, oversaturation, and/or distortions. Assuch, hardware or software may be used to reduce such defects.

SUMMARY

Example aspects of the present disclosure include:

A system for generating a corrected image according to at least oneembodiment of the present disclosure comprises an imaging devicecomprising a source configured to emit a wave and a detector configuredto receive a signal indicative of the emitted wave; a first robotic armconfigured to orient the source at a first pose; a second robotic armconfigured to orient the detector at a plurality of second poses,wherein each second pose and the first pose define a source detectordistance extending between the source and the detector; at least oneprocessor; and a memory storing data for processing by the processor,the data, when processed, causing the processor to: receive an imagedataset from the detector at each of the plurality of second poses toyield a plurality of image datasets, the plurality of datasetscomprising an initial image having a scatter effect; save the pluralityof image datasets; determine a scatter correction configured to correctthe scatter effect; and applying the correction to the initial image tocorrect the scatter effect.

Any of the aspects herein, wherein the memory saves further dataconfigured to cause the processor to: train a scatter correction modelusing a Monte-Carlo simulation to simulate a plurality of photon pathsthat forms an image, the image being at least one of a historical imageor a simulated image, wherein determining the scatter correction usesthe trained model.

Any of the aspects herein, wherein determining the scatter correctioncomprises providing the image dataset to a trained scatter correctionmodel.

Any of the aspects herein, wherein the scatter correction model istrained using a plurality of image datasets and an output of the scattercorrection model comprises a scatter correction.

Any of the aspects herein, wherein the scatter correction model istrained using at least one of one or more historical image datasets orone or more simulated image datasets and an output of the scattercorrection model comprises a scatter correction.

Any of the aspects herein, wherein the scatter correction model averagestwo or more image datasets of the plurality of image datasets.

Any of the aspects herein, wherein the second robotic arm orients thedetector independent of the first robotic arm.

Any of the aspects herein, wherein the memory saves further dataconfigured to cause the processor to: calculate a magnification factorbased on each image dataset of the plurality of image datasets and thecorresponding source detector distance, and scale the initial imageusing the magnification factor.

Any of the aspects herein, wherein the source detector distance isdetermined by subtracting a pose of the source from a pose of thedetector.

Any of the aspects herein, wherein the pose of the source and the poseof the detector may be determined from pose information received from afirst sensor of the first robotic arm and a second sensor of the secondrobotic arm.

Any of the aspects herein, wherein the plurality of second poses are ata same elevation.

Any of the aspects herein, wherein the imaging device is an X-raydevice.

A system for generating a corrected image according to at least oneembodiment of the present disclosure comprises a first robotic armconfigured to orient a source of an X-ray device at a first pose; asecond robotic arm configured to orient a detector of the X-ray deviceat a plurality of second poses, wherein each second pose and the firstpose define a source detector distance extending between the source andthe detector; at least one processor; and a memory storing data forprocessing by the processor, the data, when processed, causing theprocessor to: receive an image dataset from the detector at each of theplurality of second poses to yield a plurality of image datasets, theplurality of datasets comprising an initial image having a scattereffect; determine a scatter correction configured to correct a scattereffect; and applying the correction to the initial image to correct thescatter effect.

Any of the aspects herein, wherein determining the scatter correctionincludes determining a first set of photons and a second set of photons,the first set of photons corresponding to un-scattered photons and thesecond set of photons corresponding to scattered photons.

Any of the aspects herein, wherein the memory saves further dataconfigured to cause the processor to: calculate a magnification factorbased on each image dataset of the plurality of image datasets and thecorresponding source detector distance, and scale the initial imageusing the magnification factor.

Any of the aspects herein, wherein the memory saves further dataconfigured to cause the processor to: train a model using a Monte-Carlosimulation to simulate a plurality of photon paths that forms an image,the image being at least one of a historical image or a simulated image,wherein determining the scatter correction uses the trained model.

Any of the aspects herein, wherein training the model uses the pluralityof image datasets to yield a scatter correction.

Any of the aspects herein, wherein determining the scatter correctioncomprises providing the image dataset to a trained scatter correctionmodel.

Any of the aspects herein, wherein the scatter correction model istrained using a plurality of image datasets and an output of the scattercorrection model comprises a scatter correction.

A device for generating a corrected image according to at least oneembodiment of the present disclosure comprises at least one processor;and a memory storing data for processing by the processor, the data,when processed, causing the processor to: receive an image dataset froman imaging device having a source at a first pose and a detector at asecond pose of a plurality of second poses, the image dataset receivedat each second pose of the plurality of second poses to yield aplurality of image datasets, the plurality of datasets comprising aninitial image having a scatter effect, wherein each second pose and thefirst pose define a source detector distance extending between thesource and the detector; save the plurality of image datasets; determinea scatter correction configured to correct a scatter effect; andapplying the correction to the initial image to correct the scattereffect.

Any aspect in combination with any one or more other aspects.

Any one or more of the features disclosed herein.

Any one or more of the features as substantially disclosed herein.

Any one or more of the features as substantially disclosed herein incombination with any one or more other features as substantiallydisclosed herein.

Any one of the aspects/features/embodiments in combination with any oneor more other aspects/features/embodiments.

Use of any one or more of the aspects or features as disclosed herein.

It is to be appreciated that any feature described herein can be claimedin combination with any other feature(s) as described herein, regardlessof whether the features come from the same described embodiment.

The details of one or more aspects of the disclosure are set forth inthe accompanying drawings and the description below. Other features,objects, and advantages of the techniques described in this disclosurewill be apparent from the description and drawings, and from the claims.

The phrases “at least one”, “one or more”, and “and/or” are open-endedexpressions that are both conjunctive and disjunctive in operation. Forexample, each of the expressions “at least one of A, B and C”, “at leastone of A, B, or C”, “one or more of A, B, and C”, “one or more of A, B,or C” and “A, B, and/or C” means A alone, B alone, C alone, A and Btogether, A and C together, B and C together, or A, B and C together.When each one of A, B, and C in the above expressions refers to anelement, such as X, Y, and Z, or class of elements, such as X₁-X_(n),Y₁-Y_(m), and Z₁-Z_(o), the phrase is intended to refer to a singleelement selected from X, Y, and Z, a combination of elements selectedfrom the same class (e.g., X₁ and X₂) as well as a combination ofelements selected from two or more classes (e.g., Y₁ and Z_(o)).

The term “a” or “an” entity refers to one or more of that entity. Assuch, the terms “a” (or “an”), “one or more” and “at least one” can beused interchangeably herein. It is also to be noted that the terms“comprising”, “including”, and “having” can be used interchangeably.

The preceding is a simplified summary of the disclosure to provide anunderstanding of some aspects of the disclosure. This summary is neitheran extensive nor exhaustive overview of the disclosure and its variousaspects, embodiments, and configurations. It is intended neither toidentify key or critical elements of the disclosure nor to delineate thescope of the disclosure but to present selected concepts of thedisclosure in a simplified form as an introduction to the more detaileddescription presented below. As will be appreciated, other aspects,embodiments, and configurations of the disclosure are possibleutilizing, alone or in combination, one or more of the features setforth above or described in detail below.

Numerous additional features and advantages of the present inventionwill become apparent to those skilled in the art upon consideration ofthe embodiment descriptions provided hereinbelow.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings are incorporated into and form a part of thespecification to illustrate several examples of the present disclosure.These drawings, together with the description, explain the principles ofthe disclosure. The drawings simply illustrate preferred and alternativeexamples of how the disclosure can be made and used and are not to beconstrued as limiting the disclosure to only the illustrated anddescribed examples. Further features and advantages will become apparentfrom the following, more detailed, description of the various aspects,embodiments, and configurations of the disclosure, as illustrated by thedrawings referenced below.

FIG. 1 is a block diagram of a system according to at least oneembodiment of the present disclosure;

FIG. 2A is a schematic diagram of capturing image data at a firstdistance according to at least one embodiment of the present disclosure;

FIG. 2B is a schematic diagram of capturing image data at a seconddistance according to at least one embodiment of the present disclosure;

FIG. 3 is a flowchart according to at least one embodiment of thepresent disclosure; and

FIG. 4 is a flowchart according to at least one embodiment of thepresent disclosure.

DETAILED DESCRIPTION

It should be understood that various aspects disclosed herein may becombined in different combinations than the combinations specificallypresented in the description and accompanying drawings. It should alsobe understood that, depending on the example or embodiment, certain actsor events of any of the processes or methods described herein may beperformed in a different sequence, and/or may be added, merged, or leftout altogether (e.g., all described acts or events may not be necessaryto carry out the disclosed techniques according to different embodimentsof the present disclosure). In addition, while certain aspects of thisdisclosure are described as being performed by a single module or unitfor purposes of clarity, it should be understood that the techniques ofthis disclosure may be performed by a combination of units or modulesassociated with, for example, a computing device and/or a medicaldevice.

In one or more examples, the described methods, processes, andtechniques may be implemented in hardware, software, firmware, or anycombination thereof. If implemented in software, the functions may bestored as one or more instructions or code on a computer-readable mediumand executed by a hardware-based processing unit. Alternatively oradditionally, functions may be implemented using machine learningmodels, neural networks, artificial neural networks, or combinationsthereof (alone or in combination with instructions). Computer-readablemedia may include non-transitory computer-readable media, whichcorresponds to a tangible medium such as data storage media (e.g., RAM,ROM, EEPROM, flash memory, or any other medium that can be used to storedesired program code in the form of instructions or data structures andthat can be accessed by a computer).

Instructions or algorithms may be executed by one or more processors,such as one or more digital signal processors (DSPs), general purposemicroprocessors (e.g., Intel Core i3, i5, i7, or i9 processors; IntelCeleron processors; Intel Xeon processors; Intel Pentium processors; AMDRyzen processors; AMD Athlon processors; AMD Phenom processors; AppleA10 or 10× Fusion processors; Apple A11, A12, A12X, A12Z, or A13 Bionicprocessors; or any other general purpose microprocessors), graphicsprocessing units (e.g., Nvidia GeForce RTX 2000-series processors,Nvidia GeForce RTX 3000-series processors, AMD Radeon RX 5000-seriesprocessors, AMD Radeon RX 6000-series processors, or any other graphicsprocessing units), application specific integrated circuits (ASICs),field programmable logic arrays (FPGAs), or other equivalent integratedor discrete logic circuitry. Accordingly, the term “processor” as usedherein may refer to any of the foregoing structure or any other physicalstructure suitable for implementation of the described techniques. Also,the techniques could be fully implemented in one or more circuits orlogic elements.

Before any embodiments of the disclosure are explained in detail, it isto be understood that the disclosure is not limited in its applicationto the details of construction and the arrangement of components setforth in the following description or illustrated in the drawings. Thedisclosure is capable of other embodiments and of being practiced or ofbeing carried out in various ways. Also, it is to be understood that thephraseology and terminology used herein is for the purpose ofdescription and should not be regarded as limiting. The use of“including,” “comprising,” or “having” and variations thereof herein ismeant to encompass the items listed thereafter and equivalents thereofas well as additional items. Further, the present disclosure may useexamples to illustrate one or more aspects thereof. Unless explicitlystated otherwise, the use or listing of one or more examples (which maybe denoted by “for example,” “by way of example,” “e.g.,” “such as,” orsimilar language) is not intended to and does not limit the scope of thepresent disclosure.

The terms proximal and distal are used in this disclosure with theirconventional medical meanings, proximal being closer to the operator oruser of the system, and further from the region of surgical interest inor on the patient, and distal being closer to the region of surgicalinterest in or on the patient, and further from the operator or user ofthe system.

X-ray based imaging may be affected by scatter that occurs when photonshit an object along their path and change direction. As a result, thephotons reach another area of the detector than expected andmisrepresents a true attenuation of a scanned object. Such stray photonsmay also reduce the contrast of an image and/or increase a blurriness ofthe image.

Conventional ways to reduce scatter include using hardware such as, forexample, anti-scatter grids. Such grids may be beam-stop-arrays, whichare placed on the detector itself, and are made of many thin “walls” ina grid shape that attenuates most of the photons that are notperpendicular to the detector. However, such grids typically result inan increased X-ray dose to the patient, and may lose accuracy orfunctionality in poor conditions. Thus, another conventional solutionuses software. Such solutions attempt to estimate from the “scattered”image, an “un-scattered” object. This is usually done by one of twomethods: using Monte-Carlo simulations or deep learning techniques. TheMonte-Carlo simulations work on many synthetic models, with differentsizes and shapes. The Monte-Carlo simulations simulate the photon pathwhile considering various sorts of physical conditions and restrictions.Later, when scanning a target object, the scatter can be estimated fromthe model and can apply a correction on the X-ray projection (e.g.,image) to reduce the scatter. However, most of the corrections rely on a“predetermined” model, and very little on the actual target object.

The deep learning techniques may also use many phantom or real clinicalcases, which were scanned with and without anti-scatter grids. The deeplearning techniques may also use Monte-Carlo simulations as input. Suchdeep learning techniques may improve a run-time performance. The systemlearns the difference between the un-scattered and scattered images andapplies the necessary correction on the new projection without the grid,to look as if it was taken with a grid. For such techniques, many scansare used with and without the grid, on the specific system, which maynot always be available.

At least one embodiment of the present disclosure includes a system thatuses multiple robotic arms that can take images in any orientations andin different positions of an object. Such system may take a couple offrames of the scanned object from the same orientation, only with adifferent source detector distance (SDD). Assuming a still object, andthe same scan protocol, the differences between the projections can beused to determine the scaling of the object due to magnification and ascatter.

The magnification can be easily calculated, according to the known arms(source and detector) position and distances in each of the frames.Hence the other difference would be a result of the scatter. Thisscatter can be estimated by means of image processing and applied inconjunction with the other software options. Now the estimation woulddepend more on the actual scanned object, and less on predefined models,or general models. The results would be more tailored and a betterrepresentation of the object. Such improved images can result in abetter volume\image reconstruction and improved clinical outcome.

Embodiments of the present disclosure provide technical solutions to oneor more of the problems of (1) obtaining multiple image datasets atvarious source detector distances, (2) improving image processing toreduce or eliminate scatter, and (3) increasing patient safety byreducing radiation exposure.

Turning first to FIG. 1 , a block diagram of a system 100 according toat least one embodiment of the present disclosure is shown. The system100 may be used to generate a corrected image and/or carry out one ormore other aspects of one or more of the methods disclosed herein. Thesystem 100 comprises a computing device 102, one or more imaging devices112, a robot 114, a navigation system 118, a database 130, and/or acloud or other network 134. Systems according to other embodiments ofthe present disclosure may comprise more or fewer components than thesystem 100. For example, the system 100 may not include the imagingdevice 112, the robot 114, the navigation system 118, one or morecomponents of the computing device 102, the database 130, and/or thecloud 134.

The computing device 102 comprises a processor 104, a memory 106, acommunication interface 108, and a user interface 110. Computing devicesaccording to other embodiments of the present disclosure may comprisemore or fewer components than the computing device 102.

The processor 104 of the computing device 102 may be any processordescribed herein or any similar processor. The processor 104 may beconfigured to execute instructions stored in the memory 106, whichinstructions may cause the processor 104 to carry out one or morecomputing steps utilizing or based on data received from the imagingdevice 112, the robot 114, the navigation system 118, the database 130,and/or the cloud 134.

The memory 106 may be or comprise RAM, DRAM, SDRAM, other solid-statememory, any memory described herein, or any other tangible,non-transitory memory for storing computer-readable data and/orinstructions. The memory 106 may store information or data useful forcompleting, for example, any step of the method 400 described herein, orof any other methods. The memory 106 may store, for example, one or morealgorithms 120 and/or one or more surgical plans 122. Such algorithmsmay, in some embodiments, be organized into one or more applications,modules, packages, layers, or engines. Alternatively or additionally,the memory 106 may store other types of data (e.g., machine learningmodels, artificial neural networks, etc.) that can be processed by theprocessor 104 to carry out the various method and features describedherein. Thus, although various components of memory 106 are described asalgorithms, it should be appreciated that functionality described hereincan be achieved through use of instructions, algorithms, and/or machinelearning models. The data, algorithms, and/or instructions may cause theprocessor 104 to manipulate data stored in the memory 106 and/orreceived from or via the imaging device 112, the robot 114, the database130, and/or the cloud 134.

The computing device 102 may also comprise a communication interface108. The communication interface 108 may be used for receiving imagedata or other information from an external source (such as the imagingdevice 112, the robot 114, the navigation system 118, the database 130,the cloud 134, and/or any other system or component not part of thesystem 100), and/or for transmitting instructions, images, or otherinformation to an external system or device (e.g., another computingdevice 102, the imaging device 112, the robot 114, the navigation system118, the database 130, the cloud 134, and/or any other system orcomponent not part of the system 100). The communication interface 108may comprise one or more wired interfaces (e.g., a USB port, an ethernetport, a Firewire port) and/or one or more wireless transceivers orinterfaces (configured, for example, to transmit and/or receiveinformation via one or more wireless communication protocols such as802.11a/b/g/n, Bluetooth, NFC, ZigBee, and so forth). In someembodiments, the communication interface 108 may be useful for enablingthe device 102 to communicate with one or more other processors 104 orcomputing devices 102, whether to reduce the time needed to accomplish acomputing-intensive task or for any other reason.

The computing device 102 may also comprise one or more user interfaces110. The user interface 110 may be or comprise a keyboard, mouse,trackball, monitor, television, screen, touchscreen, and/or any otherdevice for receiving information from a user and/or for providinginformation to a user. The user interface 110 may be used, for example,to receive a user selection or other user input regarding any step ofany method described herein. Notwithstanding the foregoing, any requiredinput for any step of any method described herein may be generatedautomatically by the system 100 (e.g., by the processor 104 or anothercomponent of the system 100) or received by the system 100 from a sourceexternal to the system 100. In some embodiments, the user interface 110may be useful to allow a surgeon or other user to modify instructions tobe executed by the processor 104 according to one or more embodiments ofthe present disclosure, and/or to modify or adjust a setting of otherinformation displayed on the user interface 110 or correspondingthereto.

Although the user interface 110 is shown as part of the computing device102, in some embodiments, the computing device 102 may utilize a userinterface 110 that is housed separately from one or more remainingcomponents of the computing device 102. In some embodiments, the userinterface 110 may be located proximate one or more other components ofthe computing device 102, while in other embodiments, the user interface110 may be located remotely from one or more other components of thecomputer device 102.

The imaging device 112 may be operable to image anatomical feature(s)(e.g., a bone, veins, tissue, etc.) and/or other aspects of patientanatomy to yield image data (e.g., image data depicting or correspondingto a bone, veins, tissue, etc.). “Image data” as used herein refers tothe data generated or captured by an imaging device 112, including in amachine-readable form, a graphical/visual form, and in any other form.In various examples, the image data may comprise data corresponding toan anatomical feature of a patient, or to a portion thereof. The imagedata may be or comprise a preoperative image, an intraoperative image, apostoperative image, or an image taken independently of any surgicalprocedure. The imaging device 112 may be capable of taking a 2D image ora 3D image to yield the image data. The imaging device 112 may be orcomprise, for example, an ultrasound scanner (which may comprise, forexample, a physically separate transducer and receiver, or a singleultrasound transceiver), an O-arm, a C-arm, a G-arm, or any other deviceutilizing X-ray-based imaging (e.g., a fluoroscope, a CT scanner, orother X-ray machine), a magnetic resonance imaging (MM) scanner, anoptical coherence tomography (OCT) scanner, an endoscope, a microscope,an optical camera, a thermographic camera (e.g., an infrared camera), aradar system (which may comprise, for example, a transmitter, areceiver, a processor, and one or more antennae), or any other imagingdevice 112 suitable for obtaining images of an anatomical feature of apatient.

In some embodiments, the imaging device 112 may comprise more than oneimaging device 112. For example, a first imaging device may providefirst image data and/or a first image at a first time, and a secondimaging device may provide second image data and/or a second image atthe first time or at a second time after the first time. In still otherembodiments, the same imaging device may be used to provide both thefirst image data and the second image data, and/or any other image datadescribed herein. The imaging device 112 may be operable to generate astream of image data. For example, the imaging device 112 may beconfigured to operate with an open shutter, or with a shutter thatcontinuously alternates between open and shut so as to capturesuccessive images. For purposes of the present disclosure, unlessspecified otherwise, image data may be considered to be continuousand/or provided as an image data stream if the image data represents twoor more frames per second.

In some embodiments, the imaging device 112 may comprise a source 112Aand a detector 112B. In some embodiments, the source 112A and thedetector 112B may be in separate housings or are otherwise physicallyseparated. In such embodiments, the source 112A may be oriented by afirst robotic arm and the detector 112B may be oriented by a secondrobotic arm, as will be described in more detail below. In otherembodiments, the source 112A and the detector 112B may be in the samehousing. The source 112A may be configured to emit a wave and thedetector 112B may be configured to receive a signal indicative of theemitted wave. The detector 112B may also be configured to save aplurality of image datasets to, for example, the memory 106. The wavemay be, for example, an X-ray wave.

The robot 114 may be any surgical robot or surgical robotic system. Therobot 114 may be or comprise, for example, the Mazor X™ Stealth Editionrobotic guidance system. The robot 114 may be configured to position theimaging device 112 at one or more precise position(s) andorientation(s), and/or to return the imaging device 112 to the sameposition(s) and orientation(s) at a later point in time. The robot 114may additionally or alternatively be configured to manipulate a surgicaltool (whether based on guidance from the navigation system 118 or not)to accomplish or to assist with a surgical task. In some embodiments,the robot 114 may be configured to hold and/or manipulate an anatomicalelement during or in connection with a surgical procedure.

The robot 114 may comprise one or more robotic arms 116. The roboticarms may be controlled in a single, shared coordinate space, or inseparate coordinate spaces. In some embodiments, the robotic arm 116 maycomprise a first robotic arm and a second robotic arm, though the robot114 may comprise more than two robotic arms. In some embodiments, one ormore of the robotic arms 116 may be used to hold and/or maneuver theimaging device 112. In embodiments where the imaging device 112comprises two or more physically separate components such as, forexample, the source 112A and the detector 112B, one robotic arm 116 mayhold the source 112A, and another robotic arm 116 may hold the detector112B. Each robotic arm 116 may be accurately positionable independentlyof the other robotic arm (e.g., the detector 112B can be positioned ororiented independently of the source 112A). In some embodiments, onerobotic arm 116 may orient the source 112A at a first pose across fromthe detector 112B oriented by another robotic arm 116 at a second pose.A distance between the source 112A and the detector 112B may be referredto as a source detector distance (“SDD”) 202, as shown in FIGS. 2A-2B.The robotic arms 116 may be operable to orient the source 112A and thedetector 112B at one or more SDDs 202. In some embodiments, the source112A may remain at the same pose while the detector 112B is oriented atdifferent poses. In other embodiments, the detector 112B may remain atthe same pose while the source 112A is oriented at different poses. Instill other embodiments, both the detector 112B and the source 112B mayeach be oriented at different poses.

The robot 114, together with the robotic arm 116, may have, for example,one, two, three, four, five, six, seven, or more degrees of freedom.Further, the robotic arm 116 may be positioned or positionable in anypose, plane, and/or focal point. The pose includes a position and anorientation. As a result, an imaging device 112, surgical tool, or otherobject held by the robot 114 (or, more specifically, by the robotic arm116) may be precisely positionable in one or more needed and specificpositions and orientations.

The robotic arm(s) 116 may comprise one or more sensors that enable theprocessor 104 (or a processor of the robot 114) to determine a precisepose in space of the robotic arm (as well as any object or element heldby or secured to the robotic arm).

In some embodiments, reference markers (i.e., navigation markers) may beplaced on the robot 114 (including, e.g., on the robotic arm 116), theimaging device 112, or any other object in the surgical space. Thereference markers may be tracked by the navigation system 118, and theresults of the tracking may be used by the robot 114 and/or by anoperator of the system 100 or any component thereof. In someembodiments, the navigation system 118 can be used to track othercomponents of the system (e.g., imaging device 112) and the system canoperate without the use of the robot 114 (e.g., with the surgeonmanually manipulating the imaging device 112 and/or one or more surgicaltools, based on information and/or instructions generated by thenavigation system 118, for example).

The navigation system 118 may provide navigation for a surgeon and/or asurgical robot during an operation. The navigation system 118 may be anynow-known or future-developed navigation system, including, for example,the Medtronic StealthStation™ S8 surgical navigation system or anysuccessor thereof. The navigation system 118 may include one or morecameras or other sensor(s) for tracking one or more reference markers,navigated trackers, or other objects within the operating room or otherroom in which some or all of the system 100 is located. The one or morecameras may be optical cameras, infrared cameras, or other cameras. Insome embodiments, the navigation system may comprise one or moreelectromagnetic sensors. In various embodiments, the navigation system118 may be used to track a position and orientation (i.e., pose) of theimaging device 112, the robot 114 and/or robotic arm 116, and/or one ormore surgical tools (or, more particularly, to track a pose of anavigated tracker attached, directly or indirectly, in fixed relation tothe one or more of the foregoing). The navigation system 118 may includea display for displaying one or more images from an external source(e.g., the computing device 102, imaging device 112, or other source) orfor displaying an image and/or video stream from the one or more camerasor other sensors of the navigation system 118. In some embodiments, thesystem 100 can operate without the use of the navigation system 118. Thenavigation system 118 may be configured to provide guidance to a surgeonor other user of the system 100 or a component thereof, to the robot114, or to any other element of the system 100 regarding, for example, apose of one or more anatomical elements, whether or not a tool is in theproper trajectory, and/or how to move a tool into the proper trajectoryto carry out a surgical task according to a preoperative or othersurgical plan.

The database 130 may store, for example, one or more surgical plans 122(including, for example, pose information for an imaging device 112;steps to capture one or more image datasets; one or more settings for animaging device 112, etc.); one or more images useful in connection witha surgery to be completed by or with the assistance of one or more othercomponents of the system 100; and/or any other useful information. Thedatabase 130 may be configured to provide any such information to thecomputing device 102 or to any other device of the system 100 orexternal to the system 100, whether directly or via the cloud 134. Insome embodiments, the database 130 may be or comprise part of a hospitalimage storage system, such as a picture archiving and communicationsystem (PACS), a health information system (HIS), and/or another systemfor collecting, storing, managing, and/or transmitting electronicmedical records including image data.

The cloud 134 may be or represent the Internet or any other wide areanetwork. The computing device 102 may be connected to the cloud 134 viathe communication interface 108, using a wired connection, a wirelessconnection, or both. In some embodiments, the computing device 102 maycommunicate with the database 130 and/or an external device (e.g., acomputing device) via the cloud 134.

The system 100 or similar systems may be used, for example, to carry outone or more aspects of any of the method 400 described herein. Thesystem 100 or similar systems may also be used for other purposes.

Turning to FIGS. 2A and 2B, a schematic diagram 200 of capturing imagedata at a first SDD 202A and another schematic diagram 204 of capturingimage data at a second SDD 202B are respectively depicted. In theillustrated examples, a source 212A—which may be the same as or similarto the source 112A—is spaced from a detector 212B—which may be the sameas or similar to the detector 212B at an SDD 202. Further, as shown, oneor more objects (e.g., surgical instruments, tools, surgical implants,existing implants, etc.) and/or one or more anatomical elements 210 arepositioned between the source 212A and the detector 212B. Such one ormore objects and/or one or more anatomical elements 210 may be depictedby the resulting image datasets obtained from the source 212A and thedetector 212B. During use, the source 212A emits a wave (which may be,for example, an X-ray wave) comprising a plurality of photons 208 thatpasses through the one or more objects and/or one or more anatomicalelements 210 and the detector 212B receives a signal indicative of theplurality of photons 208.

As shown in FIGS. 2A and 2B, some of the plurality of photons 208 maymaintain a straight line through the one or more objects and/or one ormore anatomical elements 210 and will be referred to as straight orun-scattered photons 208A (depicted as a dashed line). As furtherillustrated in FIGS. 2A and 2B, straight photons or un-scattered 208Amaintain a straight line through the one or more objects and/or one ormore anatomical elements 210 regardless of the SDD 202. The un-scatteredphotons 208A may be easily identified in image datasets taken atdifferent SDDs 202. On the other hand, some of the plurality of photons208 may not maintain a straight line through the one or more objectsand/or one or more anatomical elements 210 and will be referred to asscattered photons 208B (depicted as a dash-double dot line). Scatteredphotons 208B may refract or become skewed and exit the one or moreobjects and/or one or more anatomical elements 210 at an angle and maybe received by the detector 212B at a random or different position. Suchscattered photons 208B can create a scatter effect in an image which maybe visible as, for example, noise, a change in contrast, and/or anincrease in blurriness of the image.

Straight or un-scattered photons 208A may be predictable in where theymay be received by the detector 212B whereas scattered photons 208B maybe less predictable in where they may be received by the detector 212B.By taking multiple image datasets at various SDDs 202, the un-scatteredphotons 208A and at least some of the scattered photons 208B may beidentified by observing a relationship between the photons 208 in eachimage dataset.

Also shown in FIGS. 2A and 2B, are an image dataset taken at a first SDD202A and an image dataset taken at a second SDD 202B. The second SDD202B is greater than the first SDD 202A, though the second SDD 202B insome instances may be less than the first SDD 202A. In the illustratedembodiment, the second SDD 202B (or any other SDD) is obtained bykeeping the source 212A stationary and orienting the detector 212B (by,for example, a robotic arm such as the robotic arm 116) at a distancefurther away from the source 212A than the first SSD 202A. In suchembodiments, the detector 212B remains at the same orientation. It willbe appreciated that in other embodiments, the detector 212B may beoriented at a different orientation. In other embodiments, the secondSDD 202B (or any other SDD) may be obtained by keeping the detector 212Bstationary while the source 212A is oriented at a different distanceand/or orientation from the detector 212B. In still other embodiments,the second SDD 202B (or any other SDD) may be obtained by orienting boththe detector 212B and the source 212A at different distances and/ororientations from each other.

As will be described in detail below with respect to FIG. 4 , byobtaining two or more image datasets at two or more SDDs 202, amagnification factor may be calculated based on the known SDDs 202 andthe resultant straight or un-scattered photons 208A as received by thedetector 212B. Further, a scatter correction may be determined from theimage datasets. The scatter correction may also be obtained as outputfrom a scatter correction model using the image datasets as input, whichwill be described in detail with respect to FIGS. 3 and 4 .

Turning to FIG. 3 , an example of a model architecture 300 that supportsmethods and systems (e.g., Artificial Intelligence (AI)-based methodsand/or system) for generating a corrected image is shown.

One or more image datasets 302 may be obtained by an imaging device suchas the imaging device 112 comprising a source such as the source 112A,212A and a detector such as the detector 112B, 212B that are orientedacross from each other at a corresponding one or more SDDs such as theSDDs 202. Each of the image datasets 302 may depict one or more objectsand/or one or more anatomical elements of, for example, a patient. Theplurality of image datasets 302 may comprise an initial image having ascatter effect.

The one or more image datasets 302 are received as input by a scattercorrection model 304. The scatter correction model 304 may be trainedusing one or more sets of historical image datasets, wherein at leastsome sets of historical image data may contain a plurality of knownphoton paths of a different, historical patient. In other embodiments,the scatter correction model 304 may be trained using the one or moreimage datasets 302. In still other embodiments, the scatter correctionmodel 304 may be trained using image(s) obtained from a simulation(e.g., a Monte-Carlo simulation, for example). In such embodiments, thescatter correction model 304 may be trained prior to inputting the oneor more image datasets 302 into the scatter correction model 304 or maybe trained in parallel with inputting the one or more image dataset 302into the scatter correction model 304. The scatter correction model 304may be configured determine a scatter correction 306, which may beapplied to an image, such as the initial image, to correct a scattereffect.

The scatter correction 306 and an image 312 from the plurality of imagedataset may be used by a processor such as the processor 104 as inputfor an image processing model 308. The image processing model 308 mayoutput a corrected image 310, which may be free of or substantially freeof the scatter effect. In some embodiments, the image 312 may be theinitial image of the plurality of image datasets. The image processingmodel 308 may apply the scatter correction 306 to the initial image, orany image with a scatter effect, to output the corrected image 310. Inother embodiments, the image processing model 308 may output a newlygenerated image free or substantially free of the scatter effect.

The image processing model 308 may be trained using historical scattercorrections and historical images having historical scatter effects. Inother embodiments, the image processing model 308 may be trained usingthe image 312 (which may be, for example, the initial image of the oneor more image datasets 302) and the scatter correction 306. In suchembodiments, the image processing model 308 may be trained prior toinputting the scatter correction 306 and the image 312 into the scattercorrection model 304 or may be trained in parallel with inputting thescatter correction 306 and the image 312 into the image processing model308.

FIG. 4 depicts a method 400 that may be used, for example, forgenerating a corrected image.

The method 400 (and/or one or more steps thereof) may be carried out orotherwise performed, for example, by at least one processor. The atleast one processor may be the same as or similar to the processor(s)104 of the computing device 102 described above. The at least oneprocessor may be part of a robot (such as a robot 114) or part of anavigation system (such as a navigation system 118). A processor otherthan any processor described herein may also be used to execute themethod 400. The at least one processor may perform the method 400 byexecuting instructions stored in a memory such as the memory 106. Theinstructions may correspond to one or more steps of the method 400described below. The instructions may cause the processor to execute oneor more algorithms, such as the algorithm 120.

The method 400 comprises receiving an image dataset from a detector ateach of a plurality of second poses (step 404). The detector may be thesame as or similar to the detector 112B, 212B of an imaging device suchas the imaging device 112. In some embodiments, the imaging device is anX-ray device. The imaging device may also comprise a source as thesource 112A, 212A configured to emit a wave. The detector may beconfigured to receive a signal indicative of the emitted wave. Thesource may be oriented by a first robotic arm such as the robotic arm116 at a first pose and the detector may be oriented by a second roboticarm such as the robotic arm 116 at the plurality of second poses. Insome embodiments, the plurality of second poses are each at the sameelevation. The first robotic arm may orient the source independently ofthe second robotic arm orienting the detector, and vice versa. Each ofthe plurality of second poses and the first pose define a sourcedetector distance (SDD) such as the SDD 202 extending between the sourceand the detector.

In some embodiments, the plurality of second poses and the first posemay be obtained from a surgical plan such as the surgical plan 122. Inother embodiments, the plurality of second poses and the first pose maybe input received by a user such as a surgeon or other medical provider.In still other embodiments, the plurality of second poses and the firstpose may be determined by a processor such as the processor 104 based onsettings of the imaging device, settings of one or more robotic arms,one or more objects and/or one or more anatomical elements being imaged,or the like. Receiving an image dataset from the detector at each of theplurality of second poses yields a plurality of image datasets. Theplurality of image datasets may define an initial image having a scattereffect.

The method 400 also comprises saving the plurality of image datasets(step 408). The plurality of image datasets may be saved by a processorsuch as the processor 104 to a memory such as the memory 106. Theprocessor and/or the memory may be part of the imaging device, acomputing device such as the computing device 102, or separate from theimaging device and/or the computing device. The plurality of imagedatasets may, in some embodiments, depict one or more anatomicalelements. In other embodiments, the plurality of image datasets maydepict one or more objects and/or one or more anatomical elements.

The method 400 also comprises training a scatter correction model (step412). The scatter correction model may be the same as or similar to thescatter correction model 304. An output of the scatter correction modelcomprises a scatter correction such as the scatter correction 306, whichmay be used to correct a scatter effect of an image. The scattercorrection model is trained using a plurality of image datasets, whichmay be historical image datasets, simulated image datasets, and/orcurrent image datasets. In some embodiments, training the machinelearning model may use a Monte-Carlo simulation to simulate a pluralityof photon paths that forms an image. The image may be a historical imageand/or a simulated image. The simulated image may be, for example, oneor more images obtained from a Monte-Carlo simulation.

The method 400 also comprises determining a scatter correctionconfigured to correct the scatter effect (step 416). The scattercorrection may be the same as or similar to the scatter correction 306.Determining the scatter correction may use the scatter correction modeltrained in step 412. In such embodiments, the plurality of imagedatasets received in step 404 are received as input by the scattercorrection model and an output of the scatter correction model is thescatter correction.

The scatter correction model may, in some embodiments, operate byaveraging two or more images of the plurality of image datasets todetermine a scatter correction. In other embodiments, the scattercorrection model may determine a first set of photons and a second setof photons. The first set of photons may correspond to un-scatteredphotons (such as, for example, the un-scattered photons 208A) and thesecond set of photons corresponding to scattered photons (such as, forexample, the scatter photons 208B). The scatter correction model maythen determine the scatter correction based on first set of photons andthe second set of photons.

It will be appreciated that in other embodiments, determining thescatter correction may not use the scatter correction model trained instep 412.

The method 400 also comprises applying the scatter correction to animage (step 420). The image may be, for example, the initial image ofthe plurality of image datasets having a scatter effect. Applying thescatter correction to the initial image may result in correcting ascatter effect in the initial image, which may be visible as a reductionor elimination of, for example, noise and/or blurriness in the initialimage. A contrast of the initial image may also be improved.

In some embodiments, applying the scatter correction uses an imageprocessing model such as the image processing model 308. In suchinstances, the image and the scatter correction may be used as input bya processor such as the processor 104 to the image processing model. Theimage processing model may output a corrected image, which may be freeof or substantially free of the scatter effect.

The method 400 also comprises calculating a magnification factor (step424). The magnification factor may be based on each image dataset of theplurality of image datasets and the corresponding SDD. In someembodiments, the SDD may be determined by subtracting a pose of thesource from a pose of the detector. In some embodiments, the pose of thesource may be determined from pose information received from a firstsensor of a first robotic arm orienting the source and the pose of thedetector may be determined from pose information received from a secondsensor of a second robotic arm orienting the detector. In otherembodiments, the SDD may be received from a surgical plan such as thesurgical plan 122. In still other embodiments, the SDD may be inputreceived from a user such as a surgeon or other medical provider.

The method 400 also comprises scaling the image (step 428). The imagemay be, for example, the initial image, the corrected image, or anyimage with or without a scatter effect. Scaling the image may comprisescaling the image using the magnification factor calculated in step 424.

It will be appreciated that in some embodiments, the steps 424 and 428may occur simultaneously with steps 416 and/or 420. In other words, thescatter correction may be applied to the image (such as, for example,the initial image) and the image may be scaled simultaneously. In otherembodiments, the image may be scaled prior to applying the scattercorrection or vice versa.

The present disclosure encompasses embodiments of the method 400 thatcomprise more or fewer steps than those described above, and/or one ormore steps that are different than the steps described above.

As noted above, the present disclosure encompasses methods with fewerthan all of the steps identified in FIG. 4 (and the correspondingdescription of the method 400), as well as methods that includeadditional steps beyond those identified in FIG. 4 (and thecorresponding description of the method 400). The present disclosurealso encompasses methods that comprise one or more steps from one methoddescribed herein, and one or more steps from another method describedherein. Any correlation described herein may be or comprise aregistration or any other correlation.

The foregoing is not intended to limit the disclosure to the form orforms disclosed herein. In the foregoing Detailed Description, forexample, various features of the disclosure are grouped together in oneor more aspects, embodiments, and/or configurations for the purpose ofstreamlining the disclosure. The features of the aspects, embodiments,and/or configurations of the disclosure may be combined in alternateaspects, embodiments, and/or configurations other than those discussedabove. This method of disclosure is not to be interpreted as reflectingan intention that the claims require more features than are expresslyrecited in each claim. Rather, as the following claims reflect,inventive aspects lie in less than all features of a single foregoingdisclosed aspect, embodiment, and/or configuration. Thus, the followingclaims are hereby incorporated into this Detailed Description, with eachclaim standing on its own as a separate preferred embodiment of thedisclosure.

Moreover, though the foregoing has included description of one or moreaspects, embodiments, and/or configurations and certain variations andmodifications, other variations, combinations, and modifications arewithin the scope of the disclosure, e.g., as may be within the skill andknowledge of those in the art, after understanding the presentdisclosure. It is intended to obtain rights which include alternativeaspects, embodiments, and/or configurations to the extent permitted,including alternate, interchangeable and/or equivalent structures,functions, ranges or steps to those claimed, whether or not suchalternate, interchangeable and/or equivalent structures, functions,ranges or steps are disclosed herein, and without intending to publiclydedicate any patentable subject matter.

What is claimed is:
 1. A system for generating a corrected imagecomprising: an imaging device comprising a source configured to emit awave and a detector configured to receive a signal indicative of theemitted wave; a first robotic arm configured to orient the source at afirst pose; a second robotic arm configured to orient the detector at aplurality of second poses, wherein each second pose and the first posedefine a source detector distance extending between the source and thedetector; at least one processor; and a memory storing data forprocessing by the processor, the data, when processed, causing theprocessor to: receive an image dataset from the detector at each of theplurality of second poses to yield a plurality of image datasets, theplurality of datasets comprising an initial image having a scattereffect; save the plurality of image datasets; determine a scattercorrection configured to correct the scatter effect; and applying thecorrection to the initial image to correct the scatter effect.
 2. Thesystem of claim 1, wherein the memory saves further data configured tocause the processor to: train a scatter correction model using aMonte-Carlo simulation to simulate a plurality of photon paths thatforms an image, the image being at least one of a historical image or asimulated image, wherein determining the scatter correction uses thetrained model.
 3. The system of claim 1, wherein determining the scattercorrection comprises providing the image dataset to a trained scattercorrection model.
 4. The system of claim 3, wherein the scattercorrection model is trained using a plurality of image datasets and anoutput of the scatter correction model comprises a scatter correction.5. The system of claim 3, wherein the scatter correction model istrained using at least one of one or more historical image datasets orone or more simulated image datasets and an output of the scattercorrection model comprises a scatter correction.
 6. The system of claim4, wherein the scatter correction model averages two or more imagedatasets of the plurality of image datasets.
 7. The system of claim 1,wherein the second robotic arm orients the detector independent of thefirst robotic arm.
 8. The system of claim 1, wherein the memory savesfurther data configured to cause the processor to: calculate amagnification factor based on each image dataset of the plurality ofimage datasets and the corresponding source detector distance, and scalethe initial image using the magnification factor.
 9. The system of claim8, wherein the source detector distance is determined by subtracting apose of the source from a pose of the detector.
 10. The system of claim9, wherein the pose of the source and the pose of the detector may bedetermined from pose information received from a first sensor of thefirst robotic arm and a second sensor of the second robotic arm.
 11. Thesystem of claim 1, wherein the plurality of second poses are at a sameelevation.
 12. The system of claim 1, wherein the imaging device is anX-ray device.
 13. A system for generating a corrected image comprising:a first robotic arm configured to orient a source of an X-ray device ata first pose; a second robotic arm configured to orient a detector ofthe X-ray device at a plurality of second poses, wherein each secondpose and the first pose define a source detector distance extendingbetween the source and the detector; at least one processor; and amemory storing data for processing by the processor, the data, whenprocessed, causing the processor to: receive an image dataset from thedetector at each of the plurality of second poses to yield a pluralityof image datasets, the plurality of datasets comprising an initial imagehaving a scatter effect; determine a scatter correction configured tocorrect a scatter effect; and applying the correction to the initialimage to correct the scatter effect.
 14. The system of 13, whereindetermining the scatter correction includes determining a first set ofphotons and a second set of photons, the first set of photonscorresponding to un-scattered photons and the second set of photonscorresponding to scattered photons.
 15. The system of claim 13, whereinthe memory saves further data configured to cause the processor to:calculate a magnification factor based on each image dataset of theplurality of image datasets and the corresponding source detectordistance, and scale the initial image using the magnification factor.16. The system of claim 13, wherein the memory saves further dataconfigured to cause the processor to: train a model using a Monte-Carlosimulation to simulate a plurality of photon paths that forms an image,the image being at least one of a historical image or a simulated image,wherein determining the scatter correction uses the trained model. 17.The system of claim 16, wherein training the model uses the plurality ofimage datasets to yield a scatter correction.
 18. The system of claim16, wherein determining the scatter correction comprises providing theimage dataset to a trained scatter correction model.
 19. The system ofclaim 18, wherein the scatter correction model is trained using aplurality of image datasets and an output of the scatter correctionmodel comprises a scatter correction.
 20. A device for generating acorrected image comprising: at least one processor; and a memory storingdata for processing by the processor, the data, when processed, causingthe processor to: receive an image dataset from an imaging device havinga source at a first pose and a detector at a second pose of a pluralityof second poses, the image dataset received at each second pose of theplurality of second poses to yield a plurality of image datasets, theplurality of datasets comprising an initial image having a scattereffect, wherein each second pose and the first pose define a sourcedetector distance extending between the source and the detector; savethe plurality of image datasets; determine a scatter correctionconfigured to correct a scatter effect; and applying the correction tothe initial image to correct the scatter effect.